the BrainSTORM Consortium, Anttila, V., Bulik-Sullivan, B., Finucane,H. K., Walters, R. K., Bras, J., Duncan, L., Escott-Price, V., Falcone,G. J., Gormley, P., Malik, R., Patsopoulos, N. A., Ripke, S., Wei, Z.,Yu, D., Lee, P. H., Turley, P., Grenier-Boley, B., Chouraki, V., ...Dunn, E. (2018). Analysis of shared heritability in common disordersof the brain. Science, 360(6395), [eaap8757].https://doi.org/10.1126/science.aap8757

Peer reviewed version

Link to published version (if available):10.1126/science.aap8757

Link to publication record in Explore Bristol ResearchPDF-document

This is the author accepted manuscript (AAM). The final published version (version of record) is available onlinevia AAAS at http://science.sciencemag.org/content/360/6395/eaap8757 . Please refer to any applicable terms ofuse of the publisher.

University of Bristol - Explore Bristol ResearchGeneral rights

This document is made available in accordance with publisher policies. Please cite only thepublished version using the reference above. Full terms of use are available:http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/

1

Supplementary Materials for

Analysis of Shared Heritability in Common Disorders of the Brain

Verneri Anttila…, Aiden Corvin, Benjamin M Neale, on behalf of the Brainstorm

consortium

correspondence to: verneri.anttila@gmail.com

acorvin@tcd.ie bneale@broadinstitute.org

This PDF file includes:

Materials and Methods Supplementary Text Figs. S1-S10 Tables S1-6, S8-11

Other Supplementary Materials for this manuscript includes the following:

Tables S7, S12, S13 (separate files)

2

Materials and Methods Data processing

We obtained GWAS meta-analysis summary statistics for 25 brain disorders and 17 phenotypes. Wherever non-European cohorts formed a part of those meta-analyses, we generated non-sex-stratified European-cohorts-only version of the meta-analysis of each disorder together with the primary analysts for each disorder to avoid bias stemming from ancestry differences. Prior to heritability analysis, each dataset underwent additional filtering: markers were excluded for not being present among the HapMap Project Phase 3 SNPs(84), having an allele mismatch to 1000 Genomes alleles, ambiguous strand information, INFO score <0.9 (where available), MAF<1%, and if considerable missingness in the meta-analysis was observed (where available; defined as effective per-SNP sample size less than two thirds of the 90th percentile of total sample size). To remove a potential source of bias, the major histocompatibility complex region (all SNPs on chromosome 6 between 25 and 35 Mb) was removed from all datasets, as was the region surrounding the APOE locus (all SNPs on chromosome 19 between 44 and 47 Mb) from the Alzheimer’s disease summary data.

Simulations

To evaluate the robustness of these results under various scenarios, we performed various simulations using data from the UK Biobank(85). Details about the UK Biobank project are available at http://www.ukbiobank.ac.uk. Data for the current analyses were obtained under an approved data request (application number #18597).

We used data from the interim release of 152,376 samples, originally genotyped on the UK BiLEVE Axiom array and the UK Biobank Axiom array. We filtered individuals for Caucasian ancestry and recommended removals to arrive at a final dataset of 120,267 individuals. Simulated datasets were generated to evaluate the behavior of correlation estimates 1) under different degrees of misclassification; 2) under different heritability estimates for the two traits and 3) under different liability thresholds (232 simulation conditions, 100 replicates per condition, for a total of 2.95 billion simulated individuals).

In each simulation replicate, two sets of simulated quantitative phenotypes with heritability ranging from 5-50% and prevalence ranging from 1-10% (relevant to the study phenotypes) were generated by assigning 5% of total SNPs to have simulated effect

sizes drawn from 𝑁"0, % &'

(.(*,-, where h2 is the heritability and M is the total number of

markers in the genome, standardized for minor allele frequency (p) by .2 ∗ 𝑝 ∗ (1 − 𝑝). Individual phenotypes were simulated by calculating the sum of mean centered betas multiplied by the individual’s risk alleles with the –score option in PLINK v1.90b3.38(86) and adding noise term e, drawn from 𝑁60,√1 − ℎ9:, to achieve phenotypes which sum to 𝑁(0, ℎ9). Dichotomous phenotypes were generated by assigning top 1%, 5% and 10% of each heritability simulation to be cases, and misclassification scenarios by mixing the simulated betas with those from a second, independently simulated phenotype in proportions ranging between 0-100%. Association statistics were created using an additive test in PLINK v1.90b3.38, and LDSC was used to calculate correlation estimates.

3

Simulation results were summarized to evaluate three specific scenarios considered relevant to the challenges (particularly for the psychiatric disorders, due to their spectrum-like behavior) in brain disorder co-morbidity:

1. Effect of misclassification on phenotype heritability. Given that we generally observe slightly lower heritability estimates in this study than reported in the literature with previous studies (which generally have used smaller, possibly less heterogeneous datasets), we generated 100 replicates each of simulated phenotypes at several prevalence and heritability values, and with varying degrees of misclassification of cases from a second, independent phenotype (Fig. S9A). These results demonstrate that while large-scale misclassification will impact the estimated heritability, very large misclassification proportions are required to by themselves give rise to large-scale changes in the observed heritability to the degree shown in Table S3.

2. Effect of co-morbidity on genetic correlation. Given the overlapping epidemiology of some phenotypes and the potential to observe false positive correlations due to non-trivial case misclassification, we created a range of phenotypes with varying mixing portions of correctly diagnosed cases (λ) and incorrectly diagnosed cases (1-λ) from an independent second phenotype and evaluated the genetic correlation between the hybrid phenotype and the second phenotype. This simulates the real-world scenario where e.g. (1-λ) proportion of bipolar cases would in fact be misclassified cases of schizophrenia free of bipolar disorder (Fig. S9B). We also derived a formula (see “Effect of co-morbidity and phenotypic misclassification on correlation estimates” below) to estimate the degree of misclassification required to produce the observed correlations in the absence of true genetic correlation (Table S6).

3. Effect of bidirectional comorbidity on genetic correlation. We expanded the simulation from the previous scenario given misclassification in both directions, i.e. where a proportion (1-λ) of bipolar disorder cases are misclassified schizophrenia cases and the same proportion of schizophrenia cases are misclassified bipolar disorder cases (Fig. S9C). Power calculations

Using the same methodology as described for the simulations, we created 100 replicated pairs of datasets, each with varying sample sizes (10,000, 20,000 and 40,000 individuals with a 50/50 case/control split, randomly selected from the UK Biobank data; see section above), heritabilities (1%, 5%, 10% and 20%) and polygenicity (simulating 0.5%-100% of markers contributing to the heritability). In the second set of similarly created replicates, phenotypes were additionally created to be 10%, 20%, 30%, or 40% correlated to their pair in the first set. LDSC was used to calculate the correlation between the pair (Figure S10). Heritability analysis

For a given trait, the total additive common SNP heritability in a set of GWAS summary statistics (h2g) is estimated by regressing the association χ2 statistic of a SNP against the total amount of common genetic variation tagged by that SNP (i.e., the sum of r2 between that SNP and all surrounding SNPs within a 1 Mb window, termed the LD score). The LD scores themselves, for each SNP with MAF 5-50% in the Hapmap3 data, were obtained from previously published data(87) (https://github.com/bulik/ldsc) but

4

edited by removing the at the time erroneously included HLA region markers [chr6, 20-36 Mb]. Genetic correlations, rg, (i.e., the genome-wide average shared genetic risk) for a pair of phenotypes was similarly estimated by regressing the product of Z-score for each phenotype for each SNP, instead of the χ2 statistic. The LD score referenced above is estimated from a common reference panel (for this work, the European subset of the 1000 Genomes Project reference). In this framework, including LD in the regression allows us to distinguish and account for LD-independent error sources (such as sample sharing and population stratification) from LD-dependent sources, like polygenic signal). It is essential to use an approach which is not biased by sample overlaps when analyzing summary statistics, given the large amount of control sharing between the GWAS meta-analyses in the study. P-values and effect directions for each phenotype were used to create a set of directional χ2 statistics, which were then regressed against the SNP LD scores (as the χ2 statistic is dependent on the amount of variation tagged by the SNP).

A univariate regression of these statistics against the LD statistic of each SNP was used to estimate the heritability for each phenotype using LDSC v1.0.0(88). When converting the results to liability scale, we assumed that all controls were unselected for all brain disorders as well as coronary artery disease and Crohn’s disease from the additional phenotypes (u = 1 for the formula presented in (89)). Phenotypes with a univariate heritability Z-score < 2 were excluded from further analysis (cardioembolic, large-, and small-vessel stroke and agreeableness personality measure), leaving 21 brain disorder phenotypes and 16 traits of interest. In the genetic correlation analysis, the product of χ2 statistics from the two phenotypes was similarly regressed.

Significance was assessed by Bonferroni multiple testing correction by estimating the number of independent brain disorder phenotypes by matrix decomposition of the genetic correlation results using matSpD (see Links)(90, 91). The number of independent disorder phenotypes was estimated to be 17.7943 (from 22 initial disorders, after exclusions), yielding a Bonferroni-corrected threshold of p < 3.35 x 10-4 for disorder-disorder pairs; 12.1925 independent phenotypes (from 16 initial phenotypes, after exclusions) for a threshold of p < 7.33 x 10-4 for phenotype-phenotype pairs and a total of 216.96 disorder-phenotype pairs for a threshold of p < 2.30 x 10-4. Functional enrichment and partitioning analysis

Partitioning analysis was conducted using LDSC v1.0.0(88), using stratified LD score regression to identify enriched cell type groups, expanding on the work described in Finucane et al(92). First, we obtained genome annotations for each of ten cell type groups, created by taking a union of regions with any of four histone modifications (H3K4me1, H3K4me3, H3K27ac, H3K9ac) in any cell type belonging to the cell type group. We then added each of these ten annotations to the full baseline model one at a time and performed LD score regression for each of the resulting ten models. For each of these ten analyses, we computed a Z-score for the regression coefficient corresponding to the cell type group, and we used this to test the hypothesis that the cell type group contributes positively to SNP heritability after controlling for the 53 categories in the full baseline model. Significance threshold was estimated from the number of independent phenotypes across 10 tissue categories and 53 functional categories (latter evaluated as 24 independent categories due to overlapping category structure) for Bonferroni thresholds

5

of p < 2.81 x 10-4 and 1.17 x 10-4, respectively. For the behavioral-cognitive traits and additional traits, the corresponding thresholds were p < 4.10 x 10-4 and p < 1.71 x 10-4. Correlation between heritability and dataset-specific factors

A weighted-least squares analysis was conducted among the brain disorder phenotypes in R, version 3.2, to determine what, if any, phenotype and dataset descriptive factors correlate with univariate heritability estimates. Weights were estimated using the squares of the standard errors of the univariate heritability estimates from the LD score regression analysis.

Supplementary text

Effect of co-morbidity and phenotypic misclassification on correlation estimates We derived a formula to quantify the effect of case misclassification on the

estimated genetic correlation between two traits, given the degree of misclassification, the observed heritability and the true genetic correlation. We assume both traits have similar sample and population prevalence.

Let λ be the fraction of correctly classified cases of phenotype 1, with the remainder being cases of phenotype 2 misclassified as cases of phenotype 1, β1 and β2 be true effects for phenotypes 1 and 2 on an arbitrary SNP. Therefore, the effect of the SNP on the misspecified phenotype 1 is α:

𝛼 ≡ 𝜆𝛽? + (1 − 𝜆)𝛽9

Before considering the impact on the estimated genetic correlation, we note that this

change in SNP effects means the heritability of the observed (potentially misclassified) phenotype may differ from the heritability of the true phenotype 1. Noting that the observed heritability for each phenotype is proportional to the variance of their effect sizes, we first calculate

Var(𝛼) = Var(𝜆𝛽? + (1 − 𝜆)𝛽9) = 𝜆9Var(𝛽?)+ 2𝜆(1 − 𝜆)Cov(𝛽?, 𝛽9) +(1 − 𝜆)9Var(𝛽9) = 𝜆9Var(𝛽?)+ 2𝜆(1 − 𝜆)𝑟I.Var(𝛽?)Var(𝛽9) +(1 − 𝜆)9Var(𝛽9)

Then assuming standardized regression coefficients (e.g. following the LD score

regression model), this can be written in terms of observed (obs) and true heritabilities for the two phenotypes and the number of genome-wide variants M as

ℎ?,JKL9

𝑀 = 𝜆9ℎ?9

𝑀 + 2𝜆(1 − 𝜆)𝑟INℎ?9

𝑀Nℎ9

9

𝑀 +(1 − 𝜆)9 ℎ99

𝑀

ℎ?,JKL9 = 𝜆9ℎ?9 + 2𝜆(1 − 𝜆)𝑟I%ℎ?9ℎ99 +(1 − 𝜆)9ℎ99 This allows solving for .ℎ?9 using a quadratic equation,

6

0 = 𝜆9ℎ?9 + 2𝜆(1 − 𝜆)𝑟I%ℎ?9ℎ99 +(1 − 𝜆)9ℎ99 −ℎ?,JKL9

%ℎ?9?= −2𝜆(1 − 𝜆)𝑟I.ℎ99 ±%4𝜆9(1 − 𝜆)9𝑟I9ℎ99 − 4𝜆9Q(1 − 𝜆)9ℎ99 −ℎ?,JKL9 R

2𝜆9

= −2𝜆(1 − 𝜆)𝑟I.ℎ99 ± 2𝜆%(1 − 𝜆)96𝑟I9 − 1:ℎ99 + ℎ?,JKL9

2𝜆9

= −(1 − 𝜆)𝑟I.ℎ99 ± %(1 − 𝜆)96𝑟I9 − 1:ℎ99 + ℎ?,JKL9

𝜆 Note that the sign of the first term in the numerator will be opposite of the sign of𝑟I.

Therefore if we select the sign of the phenotype so that 𝑟I > 0, then we must add the second term to ensure .ℎ?9 > 0. This gives us

%ℎ?9 = −(1 − 𝜆)𝑟I.ℎ99 + %(1 − 𝜆)96𝑟I9 − 1:ℎ99 + ℎ?,JKL9

𝜆

Note that this will not be bounded above by one when 𝜆 is small. This is not

surprising since a small 𝜆 implies that most of the cases reported for phenotype 1 are in fact cases for phenotype 2, making particular combinations of ℎ99, ℎ?,JKL9 and 𝑟I infeasible for certain values of 𝜆. From the above, the determinant of the quadratic form must be positive, thus

ℎ?,JKL9 ≥ (1 − 𝜆)961−𝑟I9:ℎ99

Similarly, the determinant of the quadratic formula, solving for ℎ99, implies

ℎ?9 ≤ℎ?,JKL9

𝜆961−𝑟I9:

This is the case unless no misclassification is present (𝜆 = 0) or the phenotypes are

functionally equivalent (𝑟I9 = 1). We can now return to the original question regarding the relationship between 𝑟I and

𝑟I,JKL in the presence of phenotype misclassification. We derive for the SNP effects of α and β2:

𝑟I,JKL ≡ Corr(𝛼, 𝛽9)

= Cov(𝛼, 𝛽9)

.Var(𝛼)Var(𝛽9)

= Cov[𝜆𝛽? + (1 − 𝜆)𝛽9, 𝛽9]

.Var(𝛼)Var(𝛽9)

7

= 𝜆Cov(𝛽?, 𝛽9) + (1 − 𝜆)Var(𝛽9)

.Var(𝛼)Var(𝛽9)

= 𝜆𝑟I.Var(𝛽?)Var(𝛽9) + (1 − 𝜆)Var(𝛽9)

.Var(𝛼)Var(𝛽9)

= 𝜆𝑟I .Var(𝛽?).Var(𝛼)

+ (1 − 𝜆).Var(𝛽9).Var(𝛼)

Again assuming standardized regression coefficients, the variances can be written in

terms of heritability as

𝑟I,JKL = 𝜆𝑟I.ℎ?9 +(1 − 𝜆).ℎ99

%ℎ?,JKL9

Rearranging and substituting the expression for .ℎ?9 from above, assuming 𝑟I > 0,

gives

𝑟I = 𝑟I,JKL%ℎ?,JKL9 − (1 − 𝜆).ℎ99

𝜆.ℎ?9

= 𝑟I,JKL%ℎ?,JKL9 − (1 − 𝜆).ℎ99

𝜆 𝜆

−(1 − 𝜆)𝑟I.ℎ99 + %(1 − 𝜆)96𝑟I9 − 1:ℎ99 + ℎ?,JKL9

=𝑟I,JKL%ℎ?,JKL9 − (1 − 𝜆).ℎ99

%(1− 𝜆)96𝑟I9 − 1:ℎ99 + ℎ?,JKL9 − (1 − 𝜆)𝑟I.ℎ99

Note that in the case with no misclassification, i.e. 𝜆 = 1,

𝑟I =𝑟I,JKL%ℎ?,JKL9

%ℎ?,JKL9 = 𝑟I,JKL

To examine the effects of co-morbidity has on the estimates, Table S5 shows

numerical solutions for the estimated true correlation of some selected disorder pairs based on literature estimates of co-morbidity, assuming unidirectional misclassification and that ℎ?,JKL9 is equal to trueℎ?9 (ie. that both disorders are roughly as heritable). For this table, we substituted the lambda values (see table for reference) and used the formula above to estimate what the true 𝑟I would be, based on the observed 𝑟I in this study. Figure S8 shows how the true genetic correlation estimates for those pairs behave across a range of λ values, given the observed 𝑟Iin this study, under the same assumptions as Table S5.

8

We further estimate what degree of unidirectional misclassification would be required to produce the significant rg values we observe in the paper, in the absence of any true correlation. Given 𝑟I = 0,

𝑟I,JKL =(1 − 𝜆).ℎ99

ℎ?,JKL9

𝜆 = 1 −𝑟I,JKLℎ?,JKL9

.ℎ99

Table S6 lists the implied values of misclassification (1-𝜆) required to produce the

observed significant 𝑟I values between brain disorders in the study, if no true correlation between the phenotypes exists.

Study-specific acknowledgments

IGAP (Alzheimer’s disease) International Genomics of Alzheimer’s Project (IGAP) is a large two-stage study

based upon genome-wide association studies (GWAS) on individuals of European ancestry. In stage 1, IGAP used genotyped and imputed data on 7,055,881 single nucleotide polymorphisms (SNPs) to meta-analyze four previously-published GWAS datasets consisting of 17,008 Alzheimer’s disease cases and 37,154 controls (The European Alzheimer’s Disease Initiative, EADI [LabEx DISTALZ - Laboratory of Excellence for Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer’s disease]; the Alzheimer Disease Genetics Consortium, ADGC [UO1AG032984]; The Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, CHARGE [R01AG033193]; The Genetic and Environmental Risk in AD consortium, GERAD [G0902227, MR/K013031/1, MR/L501517/1, ARUK-PG2014-1, MR/L023784/1, MR/M009076/1, SGR544:CADR]). This work was supported by the Framingham Heart Study’s National Heart, Lung, and Blood Institute contract (N01-HC-25195; HHSN268201500001I), by grants (R01-AG016495, R01-AG008122, R01-AG033040) from the National Institute on Aging, grant (R01-NS017950) from the National Institute of Neurological Disorders and Stroke, as well as grants , P30AG10161, R01AG15819, R01AG17917, 1R01HL128914, 2R01HL092577, and 3R01HL092577-06S1. UK sample contribution was supported by the NIHR Queen Square Dementia Biomedical Research Unit. Funding of some samples was supported by the NIHR Biomedical Research Unit (Dementia) at University College London Hospitals NHS Foundation Trust.

IHGC (Migraine) We would like to thank the numerous individuals who contributed to sample

collection, storage, handling, phenotyping and genotyping within each of the individual cohorts. We also thank the important contribution to research made by the study participants. This work was supported by the National Human Genome Research Institute of the National Institutes of Health (grant number R44HG006981) for 23andme and the UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol for ALSPAC. The Australian cohort was supported by National

9

Institutes of Health (NIH) Grants AA07535, AA07728, AA13320, AA13321, AA14041, AA11998, AA17688, DA012854, DA019951, AA010249, AA013320, AA013321, AA011998, AA017688, and DA027995; by Grants from the Australian National Health and Medical Research Council (NHMRC) (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498 and 1075175); by Grants from the Australian Research Council (ARC) (A7960034, A79906588, A79801419, DP0770096, DP0212016, and DP0343921); by the EU-funded GenomEUtwin (FP5-QLG2-CT-2002-01254) and ENGAGE (FP7-HEALTH-201413) projects; and by the European Union’s Seventh Framework program (2007–2013) under grant agreement no. 602633 (EUROHEADPAIN). DRN (FT0991022, 613674) and GWM (619667) were supported by the ARC Future Fellowship and NHMRC Fellowship Schemes. We thank P Visscher, D Duffy, A Henders, B Usher, E Souzeau, A Kuot, A McMellon, MJ Wright, MJ Campbell, A Caracella, L Bowdler, S Smith, B Haddon, A Conciatore, D Smyth, H Beeby, O Zheng and B Chapman for their input into project management, databases, phenotype collection, and sample collection, processing and genotyping. We acknowledge use of phenotype and genotype data from the British 1958 Birth Cohort DNA collection, funded by the Medical Research Council grant G0000934 and the Wellcome Trust grant 068545/Z/02. Genotyping for the B58C-WTCCC subset was funded by the Wellcome Trust grant 076113/B/04/Z. The B58C-T1DGC genotyping utilized resources provided by the Type 1 Diabetes Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF) and supported by U01 DK062418. B58C-T1DGC GWAS data were deposited by the Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research (CIMR), University of Cambridge, which is funded by Juvenile Diabetes Research Foundation International, the Wellcome Trust and the National Institute for Health Research Cambridge Biomedical Research Centre; the CIMR is in receipt of a Wellcome Trust Strategic Award (079895). The B58C-GABRIEL genotyping was supported by a contract from the European Commission Framework Programme 6 (018996) and grants from the French Ministry of Research. The Danish HC study is funded by grants from The Advanced Technology Foundation (001-2009-2), The Lundbeck Foundation (R34-A3243), and The Lundbeck Foundation Initiative for Integrative Psychiatric Research (R102-A9118). The deCODE migraine study was funded in part by the European Commission (HEALTH-FP7-2013-Innovation-602891 to the NeuroPain Consortium) and the National Institute of Dental and Craniofacial Research (NIH R01DE022905). EGCUT received targeted financing from Estonian Research Council grant IUT20-60, Center of Excellence in Genomics (EXCEGEN) and University of Tartu (SP1GVARENG). We acknowledge EGCUT technical personnel, especially Mr V. Soo and S. Smit. Data analyses were carried out in part in the High Performance Computing Center of University of Tartu. For the Finnish MA cohort, the Wellcome Trust (grants WT089062 and 098051 to A.P. ), the Academy of Finland (grants 200923, 251704, and 286500 to A.P., and 139795 to M.W.); the Academy of Finland Center of Excellence for Complex Disease Genetics; the EuroHead project (LSM-CT-2004-504837); the Academy of Finland, Center of Excellence in Complex

10

Disease Genetics, (grant numbers 213506 and 129680 to A.P. and J.K.); FP7-EUROHEADPAIN-no.602633, ENGAGE Consortium (grant agreement HEALTH-F4-2007-201413); EU/SYNSYS-Synaptic Systems (grant number 242167 to A.P.); the Sigrid Juselius Foundation, Finland (to A.P.); the Folkhälsan Research Foundation, Finland (to M.W.); Medicinska Understödsföreningen Liv & Hälsa (to M.W.), and the Helsinki University Central Hospital (to M.K., V.A.). German MA/MO (Munich): This study was supported by the Corona Foundation (S199/10052/2011). Health 2000: The Health 2000 survey was funded by the Finnish National Institute for Health and Welfare (THL) in collaboration with the National Social Insurance Institution and the five university hospital districts. VS was supported by the Academy of Finland, grant #139635, and the Finnish Foundation for Cardiovascular Research. The NFBC1966 sample received financial support from the Academy of Finland (project grants 104781, 120315, 129269, 1114194, 24300796, Center of Excellence in Complex Disease Genetics and SALVE), University Hospital Oulu, Biocenter, University of Oulu, Finland (75617), NHLBI grant 5R01HL087679-02 through the STAMPEED program (1RL1MH083268-01), NIH/NIMH (5R01MH63706:02), ENGAGE project and grant agreement HEALTH-F4-2007-201413, EU FP7 EurHEALTHAgeing -277849, the Medical Research Council, UK (G0500539, G0600705, G1002319, PrevMetSyn/SALVE) and the MRC, Centenary Early Career Award. The program is currently being funded by the H2020-633595 DynaHEALTH action and academy of Finland EGEA-project. For NTR/NESDA, we acknowledge the Netherlands Organisation for Scientific Research (NWO), the Netherlands Organisation for Health Research and Development (ZonMW), the EMGO+ Institute for Health and Care Research, the Neuroscience Campus Amsterdam, BBMRI–NL (184.021.007: Biobanking and Biomolecular Resources Research Infrastructure), the Avera Institute, Sioux Falls, South Dakota (USA), Rutgers University Cell and DNA Repository cooperative agreement [National Institute of Mental Health U24 MH068457-06], and the National Institutes of Health (NIH R01 HD042157-01A1, MH081802, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Genotyping was funded in part by grants from the National Institutes of Health (4R37DA018673-06, RC2 MH089951). The statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) which is supported by the Netherlands Scientific Organization (NWO 480-05-003), the Dutch Brain Foundation and the Department of Psychology and Education of the VU University Amsterdam. The Netherlands Twin register was also supported by ZonMW Addiction 31160008; ZonMW 940-37-024; NWO/SPI 56-464-14192; NWO-400-05-717; NWO-MW 904-61-19; NWO-MagW 480-04-004; NWO-Veni 016-115-035 and the European Research Council (ERC 230374, 284167). The Netherlands Study of Depression and Anxiety (NESDA) was also funded by ZonMW (Geestkracht program grant 10-000-1002). The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments (nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (014-93-015;

11

RIDE2), the Netherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera and Marjolein Peters, MSc, and Carolina Medina-Gomez, MSc, for their help in creating the GWAS database, and Karol Estrada, PhD, Yurii Aulchenko, PhD, and Carolina Medina-Gomez, MSc, for the creation and analysis of imputed data. The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The Swedish Twin Registry acknowledges funding support from the Swedish Research Council, Swedish Brain Foundation, Karolinska Instutet Resarch Funds, and the Åke Wibergs Stiftelse. The WGHS is supported by HL043851 and HL080467 from the National Heart, Lung, and Blood Institute and CA047988 from the National Cancer Institute with collaborative scientific support and funding for genotyping provided by Amgen. Young Finns: The Young Finns Study has been financially supported by the Academy of Finland: grants 286284 (T.L.), 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Kuopio, Tampere and Turku University Hospital Medical Funds (grant X51001 for T.L.); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation of Cardiovascular Research (T.L.); Finnish Cultural Foundation; Tampere Tuberculosis Foundation (T.L.); Emil Aaltonen Foundation (T.L.); and Yrjö Jahnsson Foundation (T.L.). We gratefully acknowledge the THL DNA laboratory for its skilful work to produce the DNA samples used in this study, and Ville Aalto and Irina Lisinen for the expert technical assistance in the statistical analyses. Ester Cuenca-Leon is supported by a 'Beatriu de Pinós' grant (2010 BP-A and BP-A2) funded by AGAUR, Generalitat de Catalunya. Phil H. Lee is supported by NIMH K99 MH101367. Tune H. Pers is supported by the Alfred Benzon Foundation.

IPDGC (Parkinson’s disease) We would like to thank all of the subjects who donated their time and biological

samples to be a part of this study. This work was supported in part by the Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS), the National Institute on Aging (NIA), and the National Institute of Environmental Health Sciences both part of the National Institutes of Health, Department of Health and Human Services; project numbers Z01-AG000949-02 and Z01-ES101986. In addition this work was supported by the Department of Defense (award W81XWH-09-2-0128), and The Michael J Fox Foundation for Parkinson’s Research. This work was supported by National Institutes of Health grants R01NS037167, R01CA141668, P50NS071674, American Parkinson Disease Association (APDA); Barnes Jewish Hospital Foundation; Greater St Louis Chapter of the APDA; Hersenstichting Nederland; Neuroscience Campus Amsterdam; and the section of medical genomics, the Prinses Beatrix Fonds. The KORA (Cooperative Research in the Region of Augsburg) research platform was started and financed by the Forschungszentrum für Umwelt und

12

Gesundheit, which is funded by the German Federal Ministry of Education, Science, Research, and Technology and by the State of Bavaria. This study was also funded by the German National Genome Network (NGFNplus number 01GS08134, German Ministry for Education and Research); by the German Federal Ministry of Education and Research (NGFN 01GR0468, PopGen); and 01EW0908 in the frame of ERA-NET NEURON and Helmholtz Alliance Mental Health in an Ageing Society (HA-215), which was funded by the Initiative and Networking Fund of the Helmholtz Association. The French GWAS work was supported by the French National Agency of Research (ANR-08-MNP-012). This study was also funded by France-Parkinson Association, the French program “Investissements d’avenir” funding (ANR-10-IAIHU-06) and a grant from Assistance Publique-Hôpitaux de Paris (PHRC, AOR-08010) for the French clinical data. This study was also sponsored by the Landspitali University Hospital Research Fund (grant to SSv); Icelandic Research Council (grant to SSv); and European Community Framework Programme 7, People Programme, and IAPP on novel genetic and phenotypic markers of Parkinson’s disease and Essential Tremor (MarkMD), contract number PIAP-GA-2008-230596 MarkMD (to HP and JHu). This study utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, Md. (http://biowulf.nih.gov), and DNA panels, samples, and clinical data from the National Institute of Neurological Disorders and Stroke Human Genetics Resource Center DNA and Cell Line Repository. People who contributed samples are acknowledged in descriptions of every panel on the repository website. We thank the French Parkinson’s Disease Genetics Study Group and the Drug Interaction with genes (DIGPD) study group: Y Agid, M Anheim, A-M Bonnet, M Borg, A Brice, E Broussolle, J-C Corvol, P Damier, A Destée, A Dürr, F Durif, A Elbaz, D Grabil, S Klebe, P. Krack, E Lohmann, L. Lacomblez, M Martinez, V Mesnage, P Pollak, O Rascol, F Tison, C Tranchant, M Vérin, F Viallet, and M Vidailhet. We also thank the members of the French 3C Consortium: C Berr, C Tzourio, and P Amouyel for allowing us to use part of the 3C cohort, and D Zelenika for support in generating the genome-wide molecular data. We thank P Tienari (Molecular Neurology Programme, Biomedicum, University of Helsinki), T Peuralinna (Department of Neurology, Helsinki University Central Hospital), L Myllykangas (Folkhalsan Institute of Genetics and Department of Pathology, University of Helsinki), and R Sulkava (Department of Public Health and General Practice Division of Geriatrics, University of Eastern Finland) for the Finnish controls (Vantaa85+ GWAS data). We used genome-wide association data generated by the Wellcome Trust Case-Control Consortium 2 (WTCCC2) from UK patients with Parkinson’s disease and UK control individuals from the 1958 Birth Cohort and National Blood Service. Genotyping of UK replication cases on ImmunoChip was part of the WTCCC2 project, which was funded by the Wellcome Trust (083948/Z/07/Z). UK population control data was made available through WTCCC1. This study was supported by the Medical Research Council and Wellcome Trust disease centre (grant WT089698/Z/09/Z to NW, JHa, and ASc). As with previous IPDGC efforts, this study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113, 085475 and 090355. This study was also supported by Parkinson’s UK (grants 8047 and J-0804) and the Medical Research Council (G0700943). We thank

13

Jeffrey Barrett for assistance with the design of the ImmunoChip. DNA extraction work that was done in the UK was undertaken at University College London Hospitals, University College London, who received a proportion of funding from the Department of Health’s National Institute for Health Research Biomedical Research Centres funding. This study was supported in part by the Wellcome Trust/Medical Research Council Joint Call in Neurodegeneration award (WT089698) to the Parkinson’s Disease Consortium (UKPDC), whose members are from the UCL Institute of Neurology, University of Sheffield, and the Medical Research Council Protein Phosphorylation Unit at the University of Dundee. The MRC (JPND), Parkinsons UK and the NIHR UCLH Biomedical Research centre are gratefully acknowledged (NWW)

METASTROKE consortium of the International Stroke Genetics Consortium

(Ischemic stroke) This project has received funding from the European Union’s Horizon 2020 research

and innovation programme under grant agreements No 666881, SVDs@target (to M Dichgans) and No 667375, CoSTREAM (to M Dichgans); the DFG as part of the Munich Cluster for Systems Neurology (EXC 1010 SyNergy) and the CRC 1123 (B3)(to M Dichgans); the Corona Foundation (to M Dichgans); the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain)(to M Dichgans); the e:Med program (e:AtheroSysMed) (to M Dichgans) and the FP7/2007-2103 European Union project CVgenes@target (grant agreement number Health-F2-2013-601456) (to M Dichgans).

Eating Disorders Working Group of the Psychiatric Genomics Consortium

(Anorexia nervosa) Data on anorexia nervosa were made possible by funding for the Genetic

Consortium on Anorexia Nervosa by the Wellcome Trust Case Control Consortium 3 project titled “A Genome-Wide Association Study of Anorexia Nervosa” (WT088827/Z/09) and the Foundation of Hope, Raleigh, North Carolina, USA. Funding for the Children’s Hospital of Pennsylvania and Price Foundation study was from the Price Foundation, the Klarman Family Foundation, Scripps Translational Sciences Institute Clinical Translational Science Award [Grant Number U54 RR0252204-01], Institute Development Award to the Center for Applied Genomics from the CHOP; 2011 - 2014 Davis Foundation Postdoctoral Fellowship Program in Eating Disorders Research Award, Yiran Guo; 2012 - 2015 Davis Foundation Postdoctoral Fellowship Program in Eating Disorders Research Award, Dong Li.

Major Depressive Disorder Working Group of the Psychiatric Genomics

Consortium This work was funded by the German Research Foundation (DFG, grant FOR2107

DA1151/5-1 to UD; SFB-TRR58, Project C09 to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant Dan3/012/17 to UD).

14

Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the Psychiatric Genomics Consortium (Tourette Syndrome and Obsessive Compulsive Disorder)

This study was supported by Grants from the National Institute of Mental Health [R01MH092290; R01MH092291; R01MH092292; R01MH092293; R01MH092513; R01MH092516; R01MH092520; R01MH092289; U24MH068457] and the New Jersey Center for Tourette Syndrome and Associated Disorders (NJCTS).

Consortium memberships

IGAP (Alzheimer’s disease) Benjamin Grenier-Boley, Vincent Chouraki, Yoichiro Kamatani, Marie-Thérèse

Bihoreau, Florence Pasquier, Olivier Hanon, Dominique Campion, Claudine Berr, Luc Letenneur, Nathalie Fievet, Alexis Brice, Didier Hannequin, Karen Ritchie, Jean-Francois Dartigues, Christophe Tzourio, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Jacques Epelbaum, Emmanuelle Duron, Bruno Dubois, Adam C. Naj, Gyungah Jun, Gary W. Beecham, Badri N. Vardarajan, Brian Kunkle, Christiane Reitz, Joseph D. Buxbaum, Paul K. Crane, Philip L. De Jager, Alison M. Goate, Matthew J. Huentelman, M. Ilyas Kamboh, Eric B. Larson, Oscar L. Lopez, Amanda J. Myers, Ekaterina Rogaeva, Peter St George-Hyslop, Debby W. Tsuang, John R. Gilbert, Hakon Hakonarson, Duane Beekly, Kara L. Hamilton-Nelson, Kelley M. Faber, Laura B. Cantwell, Deborah Blacker, David A. Bennett, Tatiana M. Foroud, Walter A. Kukull, Kathryn L. Lunetta, Eden R. Martin, Li-San Wang, Richard Mayeux, Jonathan L. Haines, Margaret A. Pericak-Vance, Lindsay A. Farrer, Gerard D. Schellenberg, Deborah C. Mash, Roger L. Albin, Liana G. Apostolova, Steven E. Arnold, Clinton T. Baldwin, Lisa L. Barnes, Thomas G. Beach, Thomas D. Bird, Bradley F. Boeve, James D. Bowen, James R. Burke, Minerva M. Carrasquillo, Steven L. Carroll, Lori B. Chibnik, David G. Clark, Carlos Cruchaga, Charles DeCarli, F. Yesim Demirci, Malcolm Dick, Kenneth B. Fallon, Martin R. Farlow, Steven Ferris, Douglas R. Galasko, Piyush Gampawar, Yasaman Saba, Marla Gearing, Daniel H. Geschwind, Neill R. Graff-Radford, Ronald L. Hamilton, John Hardy, Elizabeth Head, Lawrence S. Honig, Christine M. Hulette, Bradley T. Hyman, Gail P. Jarvik, Gregory A. Jicha, Lee-Way Jin, Anna Karydas, John S.K. Kauwe, Jeffrey A. Kaye, Ronald Kim, Edward H. Koo, Neil W Kowall, Frank M. LaFerla, James B. Leverenz, Allan I. Levey, Ge Li, Andrew P. Lieberman, Constantine G. Lyketsos, Wayne C. McCormick, Susan M. McCurry, Carol A. Miller, Jill R. Murrell, John M. Olichney, Vernon S. Pankratz, Aimee Pierce, Wayne W. Poon, Joseph F. Quinn, Murray Raskind, Barry Reisberg, Erik D. Roberson, Mary Sano, Andrew J. Saykin, Julie A. Schneider, Lon S. Schneider, William W. Seeley, Helena Schmidt, Albert V. Smith, Amanda G. Smith, Joshua A. Sonnen, Salvatore Spina, Robert A. Stern, Rudolph E. Tanzi, John Q. Trojanowski, Vivianna M. Van Deerlin, Linda J. Van Eldik, Sandra Weintraub, Kathleen A. Welsh-Bohmer, Randall L. Woltjer, Chang-En Yu, Robert Barber, Denise Harold, Rebecca Sims, Giancarlo Russo, Nicola Jones, Melanie L Dunstan, Alfredo Ramirez, Janet A Johnston, David C Rubinsztein, Michael Gill, John Gallacher, Anthony Bayer, Magda Tsolaki, Petra Proitsi, John Collinge, Nick C Fox, John Hardy, Robert Clarke, Carol Brayne, Susanne Moebus, Wolfgang Maier, Harald Hampel, Sabrina Pichler, Alison Goate, Minerva M Carrasquillo, Steven G Younkin, Michael J Owen, Michael C O'Donovan, Simon Mead, Peter Passmore, Kevin Morgan, John F Powell, John S.K.

15

Kauwe, Carlos Cruchaga, Matthias Riemenschneider, Markus M Nöthen, Lesley Jones, Peter A Holmans, Valentina Escott-Price, Julie Williams, Patrick G Kehoe, Jason D Warren, Jonathan M Schott, Natalie Ryan, Martin Rossor, Yoav Ben-Shlomo, Michelle Lupton, Steffi Riedel-Heller, Martin Dichgans, Reiner Heun, Per Hoffmann, Johannes Kornhuber, Hendrik van den Bussche, Brian Lawlor, Aoibhinn Lynch, David Mann, Isabella Heuser, Lutz Frölich, John C Morris, Andrew McQuillin, Ammar Al-Chalabi, Christopher E Shaw, Andrew B Singleton, Rita Guerreiro, Karl-Heinz Jöckel, H-Erich Wichmann, Dennis W Dickson, Ronald C Petersen, Carla A Ibrahim-Verbaas, Anita L DeStefano, Joshua C Bis, M. Arfan Ikram, Agustin Ruiz, Seung-Hoan Choi, Vilmundur Gudnason, Najaf Amin, Oscar L Lopez, Gudny Eiriksdottir, Thomas H Mosley, Jr., Tamara B Harris, Jerome I Rotter, Michael A Nalls, Palmi V Jonsson, Mercè Boada, Albert Hofman, Lenore J Launer, Cornelia M van Duijn, Sudha Seshadri, Alexa Beiser, Stephanie Debette, Qiong Yang, Galit Weinstein, Jing Wang, Andre G Uiterlinden, Peter J Koudstaal, William T Longstreth, Jr, James T Becker, Thomas Lumley, Kenneth Rice, Peter Dal-Bianco, Gerhard Ransmayr, Thomas Benke, Sven van der Lee, Jan Bressler, Myriam Fornage, Isabel Hernández, Ana Mauleón and Montserrat Alegret

IHGC (Migraine) Padhraig Gormley, Verneri Anttila, Bendik S Winsvold, Priit Palta, Tonu Esko, Kai-

How Farh, Ester Cuenca-Leon, Mikko Muona, Nicholas A Furlotte, Tobias Kurth, George McMahon, Lannie Ligthart, Gisela M Terwindt, Mikko Kallela, Rainer Malik, Caroline Ran, Scott G Gordon, Stacy Steinberg, Guntram Borck, Markku Koiranen, Hieab Adams, Terho Lehtimäki, Antti-Pekka Sarin, Juho Wedenoja, Lydia Quaye, David A Hinds, Julie E Buring, Markus Schürks, Paul M Ridker, Maria Gudlaug Hrafnsdottir, Jouke-Jan Hottenga, Brenda WJH Penninx, Markus Färkkilä, Ville Artto, Mari Kaunisto, Salli Vepsäläinen, Tobias M Freilinger, Pamela A F Madden, Nicholas G Martin, Grant W Montgomery, Eija Hamalainen, Hailiang Huang, Lude Franke, Jie Huang, Evie Stergiakouli, Phil H Lee, Cynthia Sandor, Caleb Webber, Zameel Cader, Bertram Muller-Myhsok, Stefan Schreiber, Thomas Meitinger, Johan Eriksson, Veikko Salomaa, Kauko Heikkilä, Elizabeth Loehrer, Andre G Uitterlinden, Albert Hofman, Cornelia M van Duijn, Lynn Cherkas, Audun Stubhaug, Christopher S Nielsen, Minna Männikko, Evelin Mihailov, Lili Milani, Hartmut Göbel, Ann-Louise Esserlind, Anne Francke Christensen, Thomas Folkmann Hansen, Thomas Werge, Bru Cormand, Lyn Griffiths, Marjo Hiekkala, Alfons Macaya, Patricia Pozo-Rosich, Jaakko Kaprio, Arpo J Aromaa, Olli Raitakari, M Arfan Ikram, Tim Spector, Marjo-Riitta Järvelin, Andres Metspalu, Christian Kubisch, David P Strachan, Michel D Ferrari, Andrea C Belin, Martin Dichgans, Maija Wessman, Arn MJM van den Maagdenberg, John-Anker Zwart, Dorret I Boomsma, George Davey Smith, Nicholas Eriksson, Mark J Daly, Benjamin M Neale, Jes Olesen, Daniel I. Chasman, Dale R Nyholt and Aarno Palotie

ILAE Consortium on Complex Epilepsies (Epilepsy) Richard Anney, Andreja Avbersek, Larry Baum, Felicitas Becker, Samuel Berkovic,

Jonathan Bradfield, Russell Buono, Claudia B. Catarino, Gianpiero Cavalleri, Stacey Cherny, Alison Coffey, Patrick Cossette, Gerrit-Jan Haan, Peter Jonghe, Carolien Kovel, Chantal Depondt, Dennis Dlugos, Colin Doherty, Thomas Ferraro, Martha Feucht, Andre Franke, Jacqueline French, Verena Gaus, Hongsheng Gui, Youling Guo, Hakon

16

Hakonarson, Erin Heinzen, Ingo Helbig, Helle Hjalgrim, Jennifer Jamnadas-Khoda, Michael Johnson, Reetta Kälviäinen, Anne-Mari Kantanen, Dalia Kasperaviciute, Dorothee Trenite, Bobby Koeleman, Wolfram Kunz, Patrick Kwan, Yu Lau, Holger Lerche, Costin Leu, Wolfgang Lieb, Dick Lindhout, Warren Lo, Daniel Lowenstein, Alberto Malovini, Anthony Marson, Mark McCormack, James Mills, Martina Moerzinger, Rikke Møller, Anne Molloy, Hiltrud Muhle, Ping-Wing Ng, Markus M. Nöthen, Terence O'Brien, Karen Oliver, Aarno Palotie, Michael Privitera, Rodney Radtke, Philipp Reif, Ann-Kathrin Ruppert, Thomas Sander, Theresa Scattergood, Steven Schachter, Christoph Schankin, Ingrid Scheffer, Bettina Schmitz, Susanne Schoch, Pak Sham, Sanjay Sisodiya, David Smith, Philip Smith, Michael Sperling, Michael Steffens, Pasquale Striano, Hans Stroink, Rainer Surges, Meng Tan, Neil G. Thomas, Marian Todaro, Holger Trucks, Frank Visscher, Nicole Walley, Zhi Wei, Christopher D. Whelan, Wanling Yang, Federico Zara and Fritz Zimprich

IMSGC (Multiple sclerosis) Chris Cotsapas IPDGC (Parkinson’s disease) Jose Bras, Alexis Brice, Boniface Mok, Valentina Escott-Price, Thomas Foltynie,

Thomas Gasser, Raphael Gibbs, Rita Guerreiro, John Hardy, Dena Hernandez, Peter Heutink, Peter Holmans, Suzanne Lesage, Steven Lubbe, Maria Martinez, Niccolo Mencacci, Huw Morris, Michael Nalls, Claudia Schulte, Manu Sharma, Mina Ryten, Joshua Shulman, Javier Simón-Sánchez, Andrew Singleton, Nigel Williams and Nick Wood

METASTROKE consortium of the International Stroke Genetics Consortium

(Ischemic stroke) Rainer Malik, Matthew Traylor, Sara Pulit, Steve Bevan, Jemma Hopewell,

Elizabeth Holliday, Wei Zhao, Philippe Amouyel, John Attia, Thomas Battey, Klaus Berger, Giorgio Boncoraglio, Ganesh Chauhan, Wei-Min Chen, Ioana Cotlarciuc, Stephanie Debette, Guido Falcone, Andreea Ilinca, Steven Kittner, Christina Kourkoulis, Robin Lemmens, Arne Lindgren, James Meschia, Braxton Mitchell, Joana Pera, Peter Rothwell, Pankaj Sharma, Cathie Sudlow, Turgut Tatlisumak, Vincent Thijs, Astrid Vicente, Daniel Woo, Sudha Seshadri, Jonathan Rosand, Hugh Markus, Bradford Worrall and Martin Dichgans

International Stroke Genetics Consortium Study of Intracerebral Hemorrhage

(Intracerebral Hemorrhage) Miriam Raffeld, Guido Falcone, David Tirschwell, Bradford Worrall, Agnieszka

Slowik, Magdy Selim, Jordi Jimenez-Conde, Daniel Woo, Scott Silliman, Arne Lindgren, James Meschia, Rainer Malik, Martin Dichgans and Jonathan Rosand

Attention-Deficit Hyperactivity Disorder Working Group of the Psychiatric

Genomics Consortium (Attention-deficit hyperactivity disorder) Stephen V. Faraone, Bru Cormand, Josep Antoni Ramos-Quiroga, Cristina Sánchez-

Mora, Marta Ribasés, Miguel Casas, Amaia Hervas, Maria Jesús Arranz, Yufeng Wang,

17

Jan Haavik, Tetyana Zayats, Stefan Johansson, Carol Mathews, Nigel Williams, Peter Holmans, Joanna Martin, Hakon Hakonarson, Josephine Elia, Tobias J Renner, Benno G Schimmelmann, Anke Hinney, Özgür Albayrak, Johannes Hebebrand, André Scherag, Astrid Dempfle, Beate Herpertz-Dahlmann, Judith Sinzig, Gerd Lehmkuhl, Aribert Rothenberger, Edmund J S Sonuga-Barke, Hans-Christoph Steinhausen, Jonna Kuntsi, Philip Asherson, Robert D. Oades, Tobias Banaschewski, Herbert Roeyers, Joseph Sergeant, Richard P. Ebstein, Sarah Hohmann, Barbara Franke, Jan K. Buitelaar, Lindsey Kent, Alejandro Arias Vasquez, Michael Gill, Nanda Lambregts-Rommelse, Richard JL Anney, Aisling Mulligan, Joseph Biederman, Yanli Zhang-James, Alysa E. Doyle, Andreas Reif, Klaus-Peter Lesch, Andreas Warnke, Christine M. Freitag, Susanne Walitza, Olga Rivero, Haukur Palmason, Jobst Meyer, Marcel Romanos, Anita Thapar, Kate Langley, Michael C. O’Donovan, Michael J. Owen, Marcella Rietschel, Stephanie H Witt, Soeren Dalsgaard, Preben Bo Mortensen, Anders Børglum, Ditte Demontis, Joel Gelernter, Irwin Waldman, Joel Nigg, Beth Wilmot, Nikolas Molly, Luis Rohde, Claiton Bau, Eugenio Grevet, Mara Hutz, Nina Roth Mota, Eric Mick, Sarah E. Medland, Benjamin M Neale, Raymond Walters, Mark J. Daly, Stephan Ripke, Alice Charach, Russell Schachar, Jennifer Crosbie, Sandra K. Loo, Susan L. Smalley and James J. McGough

Autism Spectrum Disorders Working Group of the Psychiatric Genomics

Consortium (Autism) Richard Anney, Stephan Ripke, Verneri Anttila, Peter Holmans, Hailiang Huang,

Phil H Lee, Sarah Medland, Benjamin Neale, Elise Robinson, Lauren Weiss, Joana Almeida, Evdokia Anagnostou, Elena Bacchelli, Joel Bader, Anthony Bailey, Vanessa Bal, Agatino Battaglia, Raphael Bernier, Catalina Betancur, Sven Bölte, Patrick Bolton, Sean Brennan, Rita Cantor, Patrícia Celestino-Soper, Andreas G. Chiocchetti, Michael Cuccaro, Geraldine Dawson, Maretha De Jonge, Silvia De Rubeis, Richard Delorme, Frederico Duque, Sean Ennis, A. Gulhan Ercan-Sencicek, Eric Fombonne, Christine M. Freitag, Louise Gallagher, John Gilbert, Arthur Goldberg, Andrew Green, Dorothy Grice, Stephen Guter, Jonathan Haines, Robert Hendren, Christina Hultman, Sabine mKlauck, Alexander Kolevzon, Christine Ladd-Acosta, Marion Leboyer, David Ledbetter, Christa Lese Martin, Pat Levitt, Jennifer Lowe, Elena Maestrini, Tiago Magalhaes, Shrikant Mane, Donna Martin, William McMahon, Alison Merikangas, Nancy Minshew, Anthony Monaco, Daniel Moreno-De-Luca, Eric Morrow, John Nurnberger, Guiomar Oliveira, Jeremy Parr, Margaret Pericak-Vance, Dalila Pinto, Regina Regan, Karola Rehnström, Abraham Reichenberg, Kathryn Roeder, Bernadette Rogé, Guy Rouleau, Sven Sandin, Gerard Schellenberg, Stephen Scherer, Stacy Steinberg, Astrid Vicente, Jacob Vorstman, Regina Waltes, Thomas Wassink, Ellen Wijsman, A. Jeremy Willsey, Lonnie Zwaigenbaum, Timothy Yu, Joseph Buxbaum, Edwin Cook, Hilary Coon, Daniel Geschwind, Michael Gill, Hakon Hakonarson, Joachim Hallmayer, Aarno Palotie, Susan Santangelo, James Sutcliffe, Dan Arking and Mark Daly

Bipolar Disorders Working Group of the Psychiatric Genomics Consortium (Bipolar

disorder) Marcella Rietschel, Thomas G Schulze, Jana Strohmaier, Robert C Thompson,

Stephanie H Witt, John Strauss, James L Kennedy, John B Vincent, Keith Matthews,

18

Shaun Purcell, Douglas Ruderfer, Pamela Sklar, Jordan Smoller, Laura J Scott, Margit Burmeister, Devin Absher, William E Bunney, Huda Akil, Ole A Andreassen, Srdjan Djurovic, Morten Mattingsdal, Ingrid Melle, Gunnar Morken, Aiden Corvin, Michael Gill, Derek Morris, Adebayo Anjorin, Nick Bass, Andrew McQuillin, Douglas Blackwood, Andrew McIntosh, Alan W McLean, Walter J Muir, Sarah E Bergen, Vishwajit Nimgaonkar, Marian L Hamshere, Christina Hultman, Mikael Landén, Paul Lichtenstein, Patrick Sullivan, Martin Schalling, Louise Frisén, Shaun Purcell, Eli Stahl, Amanda Dobbyn, Laura Huckins, Stéphane Jamain, Marion Leboyer, Bruno Etain, Frank Bellivier, Andreas J Forstner, Markus Leber, Stefan Herms, Per Hoffmann, Anna Maaser, Sascha B Fischer, Céline S Reinbold, Sarah Kittel-Schneider, Jolanta Lissowska, Janice M Fullerton, Joanna Hauser, Sarah E Medland, Scott D Gordon, Jens Treutlein, Josef Frank, Fabian Streit, José Guzman-Parra, Fermin Mayoral, Piotr M Czerski, Neonila Szeszenia-Dabrowska, Bertram Müller-Myhsok, Markus Schwarz, Peter R Schofield, Nicholas G Martin, Grant W Montgomery, Mark Lathrop, Bernhard T Baune, Sven Cichon, Thomas W Mühleisen, Franziska Degenhardt, Manuel Mattheisen, Johannes Schumacher, Wolfgang Maier, Markus M Nöthen, Michael Bauer, Philip B Mitchell, Andreas Reif, Tiffany A Greenwood, Caroline M Nievergelt, Paul D Shilling, Nicholas J Schork, Erin N Smith, John I Nurnberger, Howard J Edenberg, Tatiana Foroud, Elliot S Gershon, William B Lawson, Evaristus A Nwulia, Maria Hipolito, John Rice, William Byerley, Francis J McMahon, Wade Berrettini, James B Potash, Peter P Zandi, Sebastian Zöllner, Peng Zhang, Gerome Breen, Lisa Jones, Peter A Holmans, Ian R Jones, George Kirov, Valentina Escott-Price, Ivan Nikolov, Michael C O'Donovan, Michael J Owen, Nick Craddock, I Nicol Ferrier, Martin Alda, Pablo Cervantes, Cristiana Cruceanu, Guy A. Rouleau, Gustavo Turecki, Qingqin Li, Roel Ophoff, Rolf Adolfsson, Carlos Pato and Joanna M Biernacka

Eating Disorder Working Group of the Psychiatric Genomics Consortium (Anorexia

Nervosa) Harry Brandt, Scott Crow, Manfred M. Fichter, Katherine A. Halmi, Maria La Via,

James Mitchell, Michael Strober, Alessandro Rotondo, D. Blake Woodside, Pamela Keel, Lisa Lilenfeld, Andrew W. Bergen, Walter Kaye, Pierre Magistretti, Deborah Kaminska, Vesna Boraska Perica, Christopher S. Franklin, James A. B. Floyd, Laura M. Thornton, Laura M. Huckins, Lorraine Southam, N. William Rayner, Ioanna Tachmazidou, Kelly L. Klump, Janet Treasure, Cathryn M. Lewis, Ulrike Schmidt, Federica Tozzi, Kirsty Kiezebrink, Johannes Hebebrand, Philip Gorwood, Roger A. H. Adan, Martien J. H. Kas, Angela Favaro, Paolo Santonastaso, Fernando Fernández-Aranda, Monica Gratacos, Filip Rybakowski, Jaakko Kaprio, Anna Keski-Rahkonen, Anu Raevuori, Eric F. van Furth, Margarita C. T. Slof-Op 't Landt, James I. Hudson, Ted Reichborn-Kjennerud, Gun Peggy S. Knudsen, Palmiero Monteleone, Allan S. Kaplan, Andreas Karwautz, Hakon Hakonarson, Wade H. Berrettini, Yiran Guo, Dong Li, Nicholas J. Schork, Tetsuya Ando, Tõnu Esko, Krista Fischer, Katrin Mannik, Andres Metspalu, Jessica H. Baker, Roger D. Cone, Janiece E. DeSocio, Christopher E. Hilliard, Julie K. O'Toole, Jacques Pantel, Jin Peng Szatkiewicz, Chrysecolla Taico, Stephanie Zerwas, Sara E. Trace, Oliver S. P. Davis, Sietske Helder, Roland Burghardt, Martina de Zwaan, Karin Egberts, Stefan Ehrlich, Beate Herpertz-Dahlmann, Wolfgang Herzog, Hartmut Imgart, André Scherag, Susann Scherag, Stephan Zipfel, Nicolas Ramoz, Unna N. Danner, Carolien de Kove,

19

Judith Hendriks, Bobby P.C. Koeleman, Roel A. Ophoff, Annemarie A. van Elburg, Maurizio Clementi, Daniela Degortes, Monica Forzan, Elena Tenconi, Elisa Docampo, Susana Jimenez-Murcia, Jolanta Lissowska, Neonilia Szeszenia-Dabrowska, Joanna Hauser, Leila Karhunen, Ingrid Meulenbelt, P. Eline Slagboom, Steve Crawford, Alfonso Tortorella, Mario Maj, George Dedoussis, Fragiskos Gonidakis, Konstantinos Tziouvas, Artemis Tsitsika, Hana Papežová, Lenka Slachtova, Debora Martaskova, James L. Kennedy, Robert D. Levitan, Zeynep Yilmaz, Julia Huemer, Gudrun Wagner, Paul Lichtenstein, Gerome Breen, Sarah Cohen-Woods, Sven Cichon, Ina Giegling, Stefan Herms, Dan Rujescu, Stefan Schreiber, H-Erich Wichmann, Christian Dina, Giovanni Gambaro, Nicole Soranzo, Antonio Julia, Sara Marsal, Raquel Rabionet, Danielle M. Dick, Aarno Palotie, Samuli Ripatti, Ole A. Andreassen, Thomas Espeseth, Astri Lundervold, Ivar Reinvang, Vidar M. Steen, Stephanie Le Hellard, Morten Mattingsdal, Ioanna Ntalla, Vladimir Bencko, Lenka Foretova, Marie Navratilova, Dalila Pinto, Stephen W. Scherer, Harald Aschauer, Laura Carlberg, Alexandra Schosser, Lars Alfredsson, Bo Ding, Leonid Padyukov, Gursharan Kalsi, Marion Roberts, Darren W. Logan, Xavier Estivill, Anke Hinney, Patrick F. Sullivan, David A. Collier, Eleftheria Zeggini and Cynthia M. Bulik

Major Depressive Disorder Working Group of the Psychiatric Genomics

Consortium (Major depressive disorder) Patrick Sullivan, Stephan Ripke, Danielle Posthuma, André G Uitterlinden, Albert

Hofman, Stefan Kloiber, Klaus Berger, Bertram Müller-Myhsok, Qingqin Li, Till Andlauer, Marcella Rietschel, Andreas J Forstner, Fabian Streit, Jana Strohmaier, Josef Frank, Stefan Herms, Stephanie Witt, Jens Treutlein, Markus M. Nöthen, Sven Cichon, Franziska Degenhardt, Per Hoffmann, Thomas G. Schulze, Bernhard T Baune, Udo Dannlowski, Tracy Air, Grant Sinnamon, Naomi Wray, Andrew McIntosh, douglas blackwood, Toni-Kim Clarke, Donald MacIntyre, David Porteous, Caroline Hayward, Tonu Esko, Evelin Mihailov, Lili Milani, Andres Metspalu, Hans J Grabe, Henry Völzke, Alexander Teumer, Sandra Van der Auwera, Georg Homuth, Matthias Nauck, Cathryn Lewis, Gerome Breen, Margarita Rivera, Michael Gill, Nick Craddock, John P Rice, Michael Owen, Ole Mors, Anders Børglum, Jakob Grove, Daniel Umbricht, Carsten Horn, Christel Middeldorp, Enda Byrne, Baptiste Couvy-Duchesne, Scott Gordon, Andrew C Heath, Anjali Henders, Ian Hickie, Nicholas Martin, Sarah Medland, Grant Montgomery, Dale Nyholt, Michele Pergadia, Divya Mehta, Martin Preisig, Zoltán Kutalik, Rudolf Uher, Michael O'Donovan, Brenda WJH Penninx, Yuri Milaneschi, Wouter Peyrot, Johannes H Smit, Rick Jansen, Aartjan TF Beekman, Robert A Schoevers, Albert van Hemert, Gerard van Grootheest, Dorret Boomsma, Jouke- Jan Hottenga, Eco de Geus, Erin Dunn, Jordan Smoller, Patrik Magnusson, Nancy Pedersen, Alexander Viktorin, Thomas Werge, Thomas Folkmann Hansen, Sara Paciga, Hualin Xi, Douglas Levinson, James Potash and James A. Knowles

Tourette Syndrome and Obsessive Compulsive Disorder Working Group of the

Psychiatric Genomics Consortium (Tourette Syndrome and Obsessive-compulsive disorder)

John Alexander, Paul Arnold, Harald Aschauer, Cristina Barlassina, Cathy Barr, Robert Batterson, Laura Bellodi, Cheston Berlin, Gabriel Bedoya-Berrio, O. Bienvenu,

20

Donald Black, Michael Bloch, Rianne Blom, Helena Brentani, Lawrence W. Brown, Ruth Bruun, Cathy Budman, Christie Burton, Beatriz Camarena, Carolina Cappi, Julio Cardona-Silgado, Danielle Cath, Maria Cavallini, Keun-Ah Cheon, Barbara J. Coffey, David Conti, Edwin Cook, Nancy Cox, Bernadette Cullen, Daniele Cusi, Sabrina Darrow, Lea Davis, Dieter Deforce, Richard Delorme, Damiaan Denys, Christel Depienne, Eske Derks, Andrea Dietrich, Yves Dion, Shan Dong, Valsamma Eapen, Valsama Eapen, Karin Egberts, Lonneke Elzerman, Patrick Evans, Peter Falkai, Thomas V. Fernandez, Martijn Figee, Nelson Freimer, Odette Fründt, Abby Fyer, Blanca Garcia-Delgar, Helena Garrido, Daniel Geller, Gloria Gerber, Donald L. Gilbert, Fernando Goes, Hans-Jorgen Grabe, Marco Grados, Erica Greenberg, Benjamin Greenberg, Dorothy E. Grice, Edna Grünblatt, Julie Hagstrøm, Gregory Hanna, Andreas Hartmann, Johannes Hebebrand, Tammy Hedderly, Gary A. Heiman, Sian Hemmings, Luis Herrera, Peter Heutink, Isobel Heyman, Matthew Hirschtritt, Pieter J. Hoekstra, Hyun Ju Hong, Ana Hounie, Alden Huang, Chaim Huyser, Laura Ibanez-Gomez, Cornelia Illmann, Michael Jenike, Clare Keenan, James L. Kennedy, Judith Kidd, Kenneth Kidd, Young Key Kim, Young-Shin Kim, Robert A. King, James A. Knowles, Anuar Konkashbaev, Anastasios Konstantinidis, Sodahm Kook, Samuel Kuperman, Nuria Lanzagorta, Marion Leboyer, James Leckman, Phil H Lee, Leonhard Lennertz, Bennett L. Leventhal, Christine Lochner, Thomas Lowe, Andrea G. Ludolph, Gholson Lyon, Fabio Macciardi, Marcos Madruga-Garrido, Brion Maher, Wolfgang Maier, Irene A. Malaty, Paolo Manunta, Athanasios Maras, Carol A. Mathews, Manuel Mattheisen, James McCracken, Lauren McGrath, Nicole McLaughlin, William McMahon, Sandra Mesa Restrepo, Euripedes Miguel, Pablo Mir, Rainald Moessner, Astrid Morer, Kirsten Müller-Vahl, Alexander Münchau, Tara L. Murphy, Dennis Murphy, Benjamin Neale, Gerald Nestadt, Paul S. Nestadt, Humberto Nicolini, Markus Noethen, Erika Nurmi, William Cornejo-Ochoa, Michael S. Okun, Lisa Osiecki, Andrew Pakstis, Peristera Paschou, Michele Pato, Carlos Pato, David Pauls, John Piacentini, Christopher Pittenger, Kerstin J. Plessen, Yehuda Pollak, Danielle Posthuma, Vasily Ramensky, Eliana Mariana Ramos, Steven Rasmussen, Tobias Renner, Victor Reus, Margaret Richter, Mark Riddle, Renata Rizzo, Mary Robertson, Veit Roessner, Maria Rosário, David Rosenberg, Guy Rouleau, Stephan Ruhrmann, Andres Ruiz-Linares, Aline Sampaio, Jack Samuels, Paul Sandor, Jeremiah Scharf, Eun-Young Shin, Yin Shugart, Harvey Singer, Jan Smit, Jordan Smoller, Jungeun Song, Dan Stein, S Evelyn Stewart, Nawei Sun, Jay A. Tischfield, Fotis Tsetsos, Jennifer Tübing, Maurizio Turiel, Ana Valencia Duarte, Homero Vallada, Filip Van Nieuwerburgh, Frank Visscher, Nienke Vulink, Michael Wagner, Susanne Walitza, Sina Wanderer, Ying Wang, Sarah Weatherall, Jens Wendland, Tomasz Wolanczyk, Martin Woods, Yulia Worbe, Jinchuan Xing, Dongmei Yu, Gwyneth Zai, Ivette Zelaya, Yeting Zhang and Samuel H. Zinner

Schizophrenia Working Group of the Psychiatric Genomics Consortium

(Schizophrenia) Rolf Adolfsson, Ingrid Agartz, Ole Andreassen, Martin Begemann, Sarah Bergen, Donald Black, Douglas Blackwood, Anders Børglum, Elvira Bramon, Richard Bruggeman, Nancy Buccola, Brendan Bulik-Sullivan, Joseph Buxbaum, William Byerley, Wiepke Cahn, Murray Cairns, Dominique Campion, Rita Cantor, Vaughan Carr, Raymond Chan, Ronald Chen, Wei Cheng, Eric Cheung, Siow Chong, Sven Cichon,

21

Robert Cloninger, David Cohen, David Collier, Paul Cormican, Aiden Corvin, Nick Craddock, Benedicto Crespo-Facorro, James Crowley, David Curtis, Mark Daly, Michael Davidson, Franziska Degenhardt, Jurgen Del Favero, Lynn DeLisi, Ditte Demontis, Timothy Dinan, Srdjan Djurovic, Enrico Domenici, Gary Donohoe, Elodie Drapeau, Jubao Duan, Hannelore Ehrenreich, Johan Eriksson, Valentina Escott-Price, Tõnu Esko, Ayman Fanous, Kai-How Farh, Josef Frank, Lude Franke, Robert Freedman, Nelson Freimer, Joseph Friedman, Menachem Fromer, Pablo Gejman, Elliot Gershon, Ina Giegling, Michael Gill, Paola Giusti-Rodríguez, Stephanie Godard, Lieuwe Haan, Christian Hammer, Marian Hamshere, Thomas Hansen, Vahram Haroutunian, Annette Hartmann, Frans Henskens, Stefan Herms, Per Hoffmann, Andrea Hofman, Peter Holmans, David Hougaard, Hailiang Huang, Christina Hultman, Assen Jablensky, Inge Joa, Erik Jönsson, Antonio Julià, René Kahn, Luba Kalaydjieva, Sena Karachanak-Yankova, Brian Kelly, Kenneth Kendler, James Kennedy, Andrey Khrunin, George Kirov, Janis Klovins, Jo Knight, James A. Knowles, Bettina Konte, Vaidutis Kucinskas, Claudine Laurent, Phil H Lee, Hong Lee, Jimmy Lee Chee Keong, Bernard Lerer, Douglas Levinson, Miaoxin Li, Tao Li, Qingqin Li, Svetlana Limborska, Jianjun Liu, Carmel Loughland, Jouko Lönnqvist, Milan Macek, Patrik Magnusson, Brion Maher, Wolfgang Maier, Sara Marsal, Manuel Mattheisen, Morten Mattingsdal, Colm McDonald, Andrew McIntosh, Andrew McQuillin, Sandra Meier, Ingrid Melle, Andres Metspalu, Lili Milani, Derek Morris, Ole Mors, Preben Mortensen, Bryan Mowry, Bertram Müller-Myhsok, Kieran Murphy, Robin Murray, Benjamin Neale, Igor Nenadic, Gerald Nestadt, Annelie Nordin, Markus M. Nöthen, Eadbhard O'Callaghan, Michael O'Donovan, Sang-Yun Oh, Roel Ophoff, Jim Os, Michael Owen, Aarno Palotie, Christos Pantelis, Sergi Papiol, Michele Pato, Carlos Pato, Psychosis Endophenotypes International Consortium, Tracey Petryshen, Jonathan Pimm, Danielle Posthuma, Alkes Price, Shaun Purcell, Digby Quested, Abraham Reichenberg, Alexander Richards, Marcella Rietschel, Brien Riley, Stephan Ripke, Joshua Roffman, Panos Roussos, Douglas Ruderfer, Dan Rujescu, Veikko Salomaa, Alan Sanders, Ulrich Schall, Sibylle Schwab, Rodney Scott, Pak Sham, Jeremy Silverman, Kang Sim, Pamela Sklar, Jordan Smoller, Hon-Cheong So, David Clair, Eli Stahl, Elisabeth Stögmann, Jana Strohmaier, Scott Stroup, Mythily Subramaniam, Patrick Sullivan, Jaana Suvisaari, Jin Szatkiewicz, Srinivas Thirumalai, Draga Toncheva, Sarah Tosato, John Waddington, James Walters, Dai Wang, Qiang Wang, Bradley Webb, Mark Weiser, Thomas Werge, Dieter Wildenauer, Nigel Williams, Stephanie Witt, Emily Wong, Naomi Wray, Wellcome Trust Case-Control Consortium 2, Hualin Xi, Clement Zai and Fritz Zimprich

22

Fig. S1A. Heritability estimates for brain disorders Red bars denote psychiatric disorders, while blue bars denote neurological disorders. ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Error bars show one standard error.

23

Fig. S1B. Heritability estimates for quantitative and additional phenotypes BMI – body-mass index. Heritabilities are reported on the observed scale for quantitative phenotypes (q) and liability scale for dichotomous phenotypes (d). Error bars show one standard error.

24

Fig. S1C. Heritability and effective sample size ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95% confidence interval. Error bars show one standard error.

25

Fig. S1D. Heritability and case/control ratio ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95% confidence interval. Error bars show one standard error.

26

Fig. S1E. Heritability and disorder prevalence ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95% confidence interval. Error bars show one standard error.

27

Fig. S1F. Heritability and average age of onset for the disorder. ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Shaded area shows 95% confidence interval. Error bars show one standard error.

28

Fig. S2A. Genetic correlations against power to detect heritability

Red points show significant correlations among all disorder-disorder pairs. Two outlier values over 1 (see Table S7A) have been reduced to 1. The points close or equal to rg = 1 are pairs of a top-level disorder with a subtype of the same disorder, where high correlation is expected, ie. all migraine and migraine with aura.

29

Fig. S2B. Inverses of standard errors against power to detect heritability

Red points show significant correlations among all disorder-disorder pairs. SE – standard error.

30

Fig. S2C. Matrix of standard errors for the genetic correlations for disorder-disorder pairs.

Plotted values indicate 1/standard error * 1/25 (1/25 chosen for scaling convenience); darker shades indicate tests with more power. Six outlier values over 1 (see Table S7A) have been reduced to 1. Asterisks highlight results which are significant after Bonferroni correction. ADHD - attention deficit hyperactivity disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder.

31

Fig. S2D. Matrix of standard errors for the genetic correlations for disorder-phenotype pairs.

Plotted values indicate 1/standard error * 1/25 (1/25 chosen for scaling convenience); darker shades indicate tests with more power. 39 outlier values over 1 (see Table S7B) have been reduced to 1. Asterisks highlight results which are significant after Bonferroni correction. ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; BMI – body-mass index; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder.

32

Fig. S3A and B. Genetic correlations for attention-deficit hyperactivity disorder (top) and anorexia nervosa (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

33

Fig. S3C and D. Genetic correlations for anxiety disorders (top) and autism spectrum disorder (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

34

Fig. S3E and F. Genetic correlations for bipolar disorder (top) and major depressive disorder (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

35

Fig. S3G and H. Genetic correlations for obsessive-compulsive disorder (top) and post-traumatic stress disorder (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

36

Fig. S3I and J. Genetic correlations for schizophrenia (top) and Tourette Syndrome (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

37

Fig. S4A and B. Genetic correlations for Alzheimer’s disease (top) and epilepsy (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

38

Fig. S4C and D. Genetic correlations for focal epilepsy (top) and generalized epilepsy (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

39

Fig. S4E and F. Genetic correlations for intracerebral hemorrhage (top) and ischemic stroke (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

40

Fig. S4G and H. Genetic correlations for early-onset stroke (top) and migraine (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

41

Fig. S4I and J. Genetic correlations for migraine without aura (top) and migraine with aura (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

42

Fig. S4K and L. Genetic correlations for multiple sclerosis (top) and Parkinson’s disease (bottom). ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. P-values for correlation are shown at the end of each bar. Error bars show one standard error.

43

Fig. S5A and B. Genetic correlations for psychiatric and neurological disorders against cognitive measures. Asterisks highlight results which are significant after Bonferroni correction. ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder; ICH – intracerebral hemorrhage. Dotted line divides the psychiatric phenotypes from the neurological phenotypes. Error bars show one standard error.

44

Fig. S6A. Genetic correlations for psychiatric disorders and four personality axes. Grey sectors denote the extent of genetic correlation between each brain disorder and the four personality axes. Red line denotes zero correlation, with positive correlations on the outside and negative correlations on the inside. Error bars show one standard error. Asterisks highlight results which are significant after Bonferroni correction. Consc. – Conscientiousness; ADHD - attention deficit hyperactivity disorder; ASD – autism spectrum disorder; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder.

45

Fig. S6B. Genetic correlations for neurological disorders and four personality axes. Grey bars denote the extent of genetic correlation between each brain disorder and the four personality axes. Red line denotes zero correlation, with positive correlations on the outside and negative correlations on the inside. Error bars show one standard error. Asterisks highlight results which are significant after Bonferroni correction. Consc. – Conscientiousness; ICH – intracerebral hemorrhage.

46

Fig. S7A. Tissue category heritability enrichment analysis in psychiatric phenotypes ADHD – attention deficit hyperactivity disorder; CNS – central nervous system; GI – gastro-intestinal system; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Results for largely overlapping dataset in schizophrenia has been previously reported in Finucane et al(92). Black line denotes significance threshold for Bonferroni multiple testing correction, p=2.81 x 10-4. Only positive enrichment reported.

47

Fig. S7B. Tissue category heritability enrichment analysis in neurological phenotypes CNS – central nervous system; GI – gastro-intestinal system. Black line denotes significance threshold for Bonferroni multiple testing correction, p=2.81 x 10-4. Only positive enrichment reported.

48

Fig. S7C. Tissue category heritability enrichment analysis in quantitative and additional phenotypes BMI – body-mass index. CNS – central nervous system; GI – gastro-intestinal system. Results for identical datasets in BMI, Crohn’s disease and height have been previously reported in Finucane et al(92), and those for depressive symptoms in Okbay et al(93). The black line denotes significance threshold for Bonferroni multiple testing correction, p=4.10 x 10-4. Only positive enrichment reported.

49

Fig. S7D. Partitioned heritability analysis across 53 functional categories in study disorders ADHD - attention deficit hyperactivity disorder; ICH – intracerebral hemorrhage; MDD – major depressive disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder. Results for a largely overlapping dataset in schizophrenia have been previously reported in Finucane et al(92). The black line denotes significance threshold for Bonferroni multiple testing correction, p=1.17 x 10-4. Only positive enrichment reported.

50

Fig. S8. Effect of case misclassification on true underlying genetic correlation given the observed results. ADHD - attention deficit hyperactivity disorder; BIP – bipolar disorder; OCD – obsessive-compulsive disorder; SCZ – schizophrenia. Genetic correlations as a function of misclassification based on derivation described in the section “Effect of co-morbidity and phenotypic misclassification on correlation estimates”, for the same phenotype pairs as reported in Table S5.

51

Fig. S9A. Effect of case misclassification on observed heritability See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on correlation estimates” for details. Error bars show one standard error.

52

Fig. S9B. Effect of case misclassification on genetic correlation. See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on correlation estimates” for details. Error bars show one standard error.

53

Fig. S9C. Effect of bidirectional case misclassification on genetic correlation See Supplementary Text “Effect of co-morbidity and phenotypic misclassification on correlation estimates” for details. Error bars show one standard error.

54

Fig. S10. Power analysis for detecting genetic correlations. Shown at each combination of parameters is the fraction of simulations out of 100 replicates which detect the simulated correlation between the pair of phenotypes and are within the 95% confidence interval from the true correlation.

55

Table S1. Dataset features for the brain disorder phenotypes. ADHD – attention deficit hyperactivity disorder; OCD – obsessive-compulsive disorder; PTSD – post-traumatic stress disorder; Pop. prev. – population prevalence; AOE – average age of onset; GC – genomic control; Publ. – publication for genotype data; MUP – manuscript under preparation; Preval. ref. – publication for prevalence estimate; PC – personal communication. All age of onset estimates based on personal communication and constitute rough estimates. Anxiety disorders refers to a meta-analysis of five subtypes (generalized anxiety disorder, panic disorder, social phobia, agoraphobia and specific phobias; see reference). Numbers in gray denote a dataset which is non-unique, e.g. all cardioembolic stroke cases and controls are also part of ischemic stroke cases and controls, respectively. For genomic control, - : no GC; + : study-level GC; ++ : meta-analysis GC. Note: genomic control will impact the univariate estimate of heritability, but the genetic correlation estimation is robust to genomic control. References are: a(94), b(95), c(96), d(97), e(98), f(99), g(100), h(101), i(102), j(103), k(104), l(105), m(106), n(107), o(108), p(109), q(110), r(111), s(112), approximated from t(113), approximated from u(114), v(115), w(116), and x(117).

Phenotype Cases Controls Pop. prev. AOE Heritability (SE) GC Mean χ2 Lambda Intercept (SE) Publ. Preval. ref.Psychiatric disordersADHD 12,645 84,435 0.050 12 0.100 (0.011) - 1.102 1.107 1.014 (0.007) MUP mAnorexia nervosa 3,495 11,105 0.006 15 0.172 (0.027) + 1.077 1.086 1.012 (0.008) a nAnxiety disorders 5,761 11,765 0.100 11 0.112 (0.045) - 1.035 1.030 1.003 (0.008) b oAutism spectrum disorder 6,197 7,377 0.010 2 0.189 (0.025) - 1.081 1.071 0.987 (0.009) c mBipolar disorder 20,352 31,358 0.010 25 0.205 (0.010) - 1.324 1.387 1.021 (0.010) MUP mMajor depressive disorder 16,823 25,632 0.150 32 0.112 (0.006) - 1.263 1.293 1.005 (0.010) MUP mOCD 2,936 7,279 0.016 16 0.255 (0.037) - 1.059 1.065 1.000 (0.007) MUP pPTSD 2,424 7,113 0.080 23 0.148 (0.065) - 1.107 1.102 1.014 (0.007) d qSchizophrenia 33,640 43,456 0.010 21 0.256 (0.010) - 1.588 1.768 1.059 (0.012) e mTourette's syndrome 4,220 8,994 0.005 7 0.196 (0.025) - 1.096 1.103 1.010 (0.007) MUP rNeurological disordersAlzheimer's disease 17,008 37,154 0.170 65 0.130 (0.023) - 1.093 1.104 1.038 (0.007) f PCEpilepsy 7,779 20,439 0.030 25 0.101 (0.022) + 1.047 1.057 0.993 (0.010) g s Focal epilepsy 4,601 17,985 0.020 15 0.053 (0.026) + 1.023 1.013 0.988 (0.009) g PC Generalized epilepsy 2,525 16,244 0.008 15 0.351 (0.039) + 1.065 1.081 0.960 (0.009) g PCIntracerebral hemorrhage 1,545 1,481 0.002 70 0.156 (0.060) - 1.038 1.037 1.012 (0.007) h tIschemic stroke 10,307 19,326 0.010 71 0.038 (0.010) - 1.065 1.066 1.032 (0.006) i u Cardioembolic stroke 1,859 17,708 - - - - 1.061 1.047 1.049 (0.006) i - Early-onset stroke 3,274 11,012 0.003 50 0.051 (0.020) - 1.029 1.033 1.009 (0.007) i PC Large-vessel disease 1,817 17,708 - - - - 1.061 1.053 1.052 (0.006) i - Small-vessel disease 1,349 17,708 - - - - 1.048 1.047 1.052 (0.006) i -Migraine 59,673 316,078 0.160 30 0.150 (0.007) - 1.293 1.375 1.036 (0.010) j v Migraine with aura 6,332 142,817 0.075 30 0.124 (0.024) - 1.077 1.087 1.003 (0.007) j w Migraine without aura 8,348 136,758 0.130 30 0.208 (0.025) - 1.080 1.085 1.033 (0.007) j wMultiple sclerosis 5,545 12,153 0.002 30 0.141 (0.016) ++ 1.050 1.078 0.975 (0.008) k xParkinson's disease 5,333 12,019 0.002 60 0.105 (0.017) + 1.026 1.044 0.965 (0.008) l x

56

Table S2. Dataset features for the behavioral-cognitive and additional phenotypes Numbers in gray denote a sample set which is non-unique, e.g. all samples in the BMI analysis are also part of the height analysis. SE – standard error; Ref. – reference; ISCE - International Standard Classification of Education (1997); NEO-FFI - Neuroticism-Extraversion-Openness Five-Factor Inventory; BMI – body-mass index; CAD – coronary artery disease; MI – myocardial infarction; (d) – dichotomous phenotype; (q) – quantitative phenotype. References are: a(118), b(119), c(120), d(121), e(93), f(122), g(123), h(124), i(125), j(126), k(127) and l(128).

Phenotype n Heritability (SE) Mean χ2 Lambda Intercept (SE) Ref. DefinitionCognitive measures Years of education (q) 293,723 0.302 (0.010) 1.645 1.475 0.938 (0.009) a Years of schooling, measured with the ISCE scale College attainment (d) 120,917 0.109 (0.008) 1.223 1.194 1.021 (0.009) b College completion (ISCE scale value >=5) Cognitive performance (q) 17,989 0.191 (0.031) 1.075 1.065 1.001 (0.009) c General cognitive ability in childhood (ages 6-18) Intelligence (q) 78,308 0.194 (0.010) 1.299 1.260 1.015 (0.008) d Intelligence measures (fluid intelligence scores in

adults or general cognitive ability in children)Personality measures Subjective well-being (q) 298,420 0.062 (0.005) 1.152 1.130 1.001 (0.007) e Self-assessed psychological well-being, based on

positive affect or life satisfaction questionnaires Depressive symptoms (q) 161,460 0.063 (0.005) 1.153 1.133 1.000 (0.007) e Score for depressive symptoms, based on positive

affect or life satisfaction questionnaires Neuroticism (q) 170,911 0.125 (0.010) 1.307 1.237 0.994 (0.010) e Personality score for neuroticism symptoms, based on

positive affect or life satisfaction questionnaires Extraversion (q) 63,030 0.049 (0.008) 1.073 1.065 1.008 (0.007) f Extraversion personality trait, as measured by several

different questionnaires Agreeableness (q) 17,375 - 1.010 0.999 1.001 (0.010) g NEO-FFI questionnaire for personality scores Conscientiousness (q) 17,375 0.070 (0.033) 1.029 1.020 1.001 (0.009) g NEO-FFI questionnaire for personality scores Openness (q) 17,375 0.125 (0.030) 1.037 1.041 0.988 (0.009) g NEO-FFI questionnaire for personality scoresSmoking-related measures Never/ever smoked (d) 74,035 0.120 (0.010) 1.103 1.090 0.996 (0.006) h Lifetime cigarette consumption >= 100 Cigarettes per day (q) 38,617 0.057 (0.013) 1.049 1.053 1.007 (0.006) h Average or maximum number of cigarettes per dayAdditional phenotypes BMI (q) 339,224 0.109 (0.003) 1.158 1.038 0.672 (0.008) i BMI, as measured Height (q) 253,288 0.312 (0.014) 2.949 2.001 1.325 (0.019) j Height, as measured Coronary artery disease (d) 86,995 0.098 (0.013) 1.145 1.105 1.027 (0.009) k Presence of CAD, MI, or both Crohn's disease (d) 20,883 0.177 (0.021) 1.242 1.143 1.028 (0.012) l Presence of Crohn’s disease

57

Table S3. Comparison of heritability estimates in this study with previously reported estimates based on SNP data. ADHD – attention deficit hyperactivity disorder; ESS – effective sample size; OCD – obsessive-compulsive disorder; SE – standard error. References previous reports are: a(106), b(95), c(129), d(97), e(130), f(131), g(132), h(133), and i(134). * - Previously reported heritability for anxiety disorders is an LDSC analysis of the same dataset; difference between the estimates is due to the current study estimating heritability under unscreened controls.

Previously reported Current studyPhenotype Heritability (SE) ESS Heritability (SE) ESS ReferencePsychiatric disordersADHD 0.28 (0.023) 12,374 0.100 (0.011) 43,992 aAnorexia nervosa - - 0.172 (0.027) 10,633 -Anxiety disorders* 0.10 (0.037) 15,469 0.112 (0.045) 15,469 bAutism spectrum disorder 0.17 (0.025) 6,729 0.189 (0.025) 10,610 aBipolar disorder 0.25 (0.012) 15,391 0.205 (0.010) 49,367 aMajor depressive disorder 0.21 (0.021) 18,416 0.112 (0.006) 40,627 aOCD 0.37 (0.070) 3,394 0.255 (0.037) 8,369 cPTSD* 0.15(0.060) 7,232 0.148 (0.065) 7,232 dSchizophrenia 0.23 (0.008) 20,811 0.256 (0.010) 75,846 aTourette's syndrome 0.58 (0.090) 2,146 0.196 (0.025) 11,489 c

Neurological disorders 0.130 (0.023)Alzheimer's disease 0.24 (0.030) 7,095 0.101 (0.022) 46,669 eEpilepsy 0.32 (0.046) 4,041 0.053 (0.026) 22,538 f Focal epilepsy 0.23 (0.102) 3,229 0.351 (0.039) 14,655 f Generalized epilepsy 0.36 (0.117) 1,134 0.156 (0.060) 8,741 fIntracerebral hemorrhage 0.29 (0.110) 1,663 0.038 (0.010) 3,025 gIschemic stroke 0.38 (0.052) 8,025 - 26,888 h Cardioembolic stroke 0.33 (0.074) 2,592 0.051 (0.020) 6,730 h Early-onset stroke - - - 10,095 h Large-vessel disease 0.40 (0.076) 2,698 - 6,592 - Small-vessel disease 0.16 (0.077) 1,993 0.150 (0.007) 5,014 hMigraine - - 0.124 (0.024) 200,785 - Migraine with aura - - 0.208 (0.025) 24,253 - Migraine without aura - - 0.141 (0.016) 31,471 -Multiple sclerosis 0.30 (0.030) 3,523 0.105 (0.017) 15,231 eParkinson's disease 0.27 (0.053) 20,798 0.105 (0.017) 14,776 i

58

Table S4. Heritability estimates and selected study variables in weighted-least squares analysis. P-values are uncorrected for multiple testing. Age of onset refers to the average age of onset of the disorder. Asterisk indicates results which are significant after Bonferroni correction for four tests.

Table S5. Implied true correlations between selected phenotypes, given co-morbidity estimates from literature. ADHD – attention deficit hyperactivity disorder; OCD – obsessive-compulsive disorder. References used for λ values (proportion of cases correctly called cases) are a (135), b(136), c(137). From reference a, λ was calculated by summing over all relevant disorder progression paths. See Supplementary text (“Effect of co-morbidity and phenotypic misclassification on correlation estimates”) for further details.

Study feature F-statistic Adjusted R2 P-valueCase/control ratio 0.534 -0.023 0.474Effective sample size 1.039 0.002 0.320Phenotype prevalence 0.341 -0.032 0.566Age of onset 10.280 0.307 0.004*

Phenotype 1 Phenotype 2 True rg Observed rg λ h1,obs h2 ReferenceSchizophrenia Bipolar disorder 0.654 0.681 0.946 0.506 0.453 aSchizophrenia OCD 0.120 0.428 0.683 0.506 0.505 bBipolar disorder ADHD 0.043 0.261 0.686 0.453 0.316 cBipolar disorder Schizophrenia 0.514 0.681 0.808 0.453 0.506 a

59

Table S6. Proportions of unidirectional misclassification. Listed are the proportions of unidirectional misclassification which would be required to reach the observed genetic correlation under the assumption of no true genetic correlation for the significantly correlated disorder-disorder pairs in this study, in order of decreasing significance. ADHD – attention deficit hyperactivity disorder; ASD – autism spectrum disorder; MDD – major depressive disorder; OCD – obsessive-compulsive disorder. Type-subtype pairs (e.g. epilepsy and focal epilepsy) have been excluded.

Table S7 (separate file). Disorder-disorder (A), disorder-phenotype (B) and phenotype-phenotype (C) correlation results.

Phenotype 1 Phenotype 2 Observed rg Misclassification %Bipolar disorder Schizophrenia 0.681 54.5%MDD Schizophrenia 0.338 14.8%Bipolar disorder MDD 0.351 64.2%MDD Migraine 0.323 24.8%ADHD MDD 0.521 46.5%OCD Schizophrenia 0.327 32.6%ADHD Migraine 0.261 17.9%Anxiety disorders MDD 0.794 79.4%ADHD Schizophrenia 0.223 8.7%OCD Tourette Syndrome 0.428 55.7%Bipolar disorder OCD 0.311 25.0%ADHD Bipolar disorder 0.261 12.7%Anorexia nervosa OCD 0.517 34.9%Migraine Tourette Syndrome 0.192 14.3%MDD Migraine without aura 0.225 12.2%Anorexia nervosa Schizophrenia 0.219 14.7%MDD Migraine with aura 0.278 29.4%MDD Tourette Syndrome 0.213 12.2%MDD OCD 0.228 10.0%ASD Schizophrenia 0.208 15.5%

60

Table S8. Tissue enrichment analysis for brain disorders. Results shown for phenotype-tissue pairs where P-value for enrichment coefficient p-value below the Bonferroni threshold (p < 2.81 x 10-4; data for all pairs in Table S12A). CNS – central nervous system; Coeff. – coefficient; SE – standard error. Results for schizophrenia in a largely overlapping dataset have been previously reported in Finucane et al (38).

Table S9. Tissue enrichment analysis for behavioral-cognitive phenotypes and additional traits Results shown for phenotype-tissue pairs where P-value for enrichment coefficient p-value below the Bonferroni threshold (p < 4.10 x 10-4; data for all pairs in Table S12A). BMI – body-mass index; CNS – central nervous system; Coeff. – coefficient; SE – standard error. Results for the same dataset in height, BMI and Crohn’s disease have been previously reported in Finucane et al (38), and depressive symptoms in Okbay et al(93).

Table S10. Functional category enrichment analysis for brain disorders Results shown for phenotype-tissue pairs where P-value for enrichment coefficient p-value below the Bonferroni threshold (p < 1.17 x 10-4; data for all pairs in Table S12B). ADHD – attention deficit hyperactivity disorder; Coeff. – coefficient; SE – standard error. Results for schizophrenia in a largely overlapping dataset have been previously reported in Finucane et al (38).

Phenotype Tissue Enrichment SE Coeff. SE Coeff. p-valueSchizophrenia CNS 3.25 0.18 1.65E-07 1.92E-08 3.35E-18Bipolar disorder CNS 3.81 0.32 1.43E-07 2.28E-08 1.69E-10Major depressive disorder CNS 2.76 0.30 2.03E-08 3.58E-09 6.73E-09Multiple sclerosis Hematopoietic 4.90 0.54 2.85E-07 5.44E-08 8.03E-08Tourette Syndrome CNS 4.23 0.78 2.32E-07 5.29E-08 5.67E-06Generalized epilepsy CNS 2.79 0.60 1.68E-07 4.34E-08 5.44E-05

Phenotype Tissue Enrichment SE Coeff. SE Coeff. p-valueYears of education CNS 2.87 0.19 9.51E-08 1.15E-08 5.66E-17Intelligence CNS 3.38 0.31 8.34E-08 1.20E-08 2.26E-12Height Connective_Bone 5.32 0.38 2.24E-07 3.55E-08 1.31E-10BMI CNS 2.67 0.18 2.73E-08 4.46E-09 4.39E-10Crohn's disease Hematopoietic 4.19 0.43 3.60E-07 6.66E-08 3.15E-08Neuroticism CNS 2.47 0.29 3.83E-08 8.45E-09 2.95E-06College attainment CNS 3.31 0.45 2.71E-08 6.52E-09 1.59E-05Height Cardiovascular 4.23 0.38 1.28E-07 3.08E-08 1.66E-05Height Other 3.42 0.21 8.26E-08 2.19E-08 7.84E-05Depressive symptoms Adrenal_Pancreas 5.15 0.94 3.77E-08 1.04E-08 1.47E-04Never/ever smoked CNS 3.45 0.73 3.06E-08 9.13E-09 4.04E-04

Phenotype Category Enrichment SE Coeff. SE Coeff. p-valueMajor depressive disorder Conserved_LindbladToh 19.14 2.50 1.74E-07 2.52E-08 2.42E-12Migraine Conserved_LindbladToh 16.88 2.08 1.09E-07 1.72E-08 1.15E-10Schizophrenia Conserved_LindbladToh 11.03 1.55 6.73E-07 1.21E-07 1.27E-08ADHD Conserved_LindbladToh 27.15 6.24 2.00E-07 4.61E-08 7.46E-06Migraine without aura Conserved_LindbladToh 20.64 4.99 9.63E-08 2.61E-08 1.12E-04Bipolar disorder Conserved_LindbladToh 9.95 1.98 3.80E-07 1.03E-07 1.17E-04

61

Table S11. Functional category enrichment analysis for behavioral-cognitive phenotypes and additional traits Results shown for phenotype-tissue pairs where P-value for enrichment coefficient p-value below the Bonferroni threshold (p < 1.71 x 10-4; data for all pairs in Table S12B). BMI – body-mass index; Coeff. – coefficient; SE – standard error. Results for the same dataset in height and BMI have been previously reported in Finucane et al (38).

Table S12 (separate file). Tissue (A) and functional category (B) enrichment analysis results for brain disorders, behavioral-cognitive phenotypes, and additional traits.

Table S13 (separate file). Data sources, responsible consortia, and data availability.

Phenotype Category Enrichment SE Coeff. SE Coeff. p-valueBMI Conserved_LindbladToh 16.68 1.69 2.76E-07 3.34E-08 7.28E-17Years of education Conserved_LindbladToh 14.96 1.68 6.56E-07 8.80E-08 4.67E-14Height Conserved_LindbladToh 11.07 1.59 5.15E-07 9.79E-08 7.20E-08BMI H3K9ac_peaks_Trynka 7.00 0.97 1.18E-07 2.41E-08 4.79E-07Neuroticism Conserved_LindbladToh 11.96 2.95 2.26E-07 5.10E-08 4.50E-06Intelligence Conserved_LindbladToh 13.50 2.56 3.51E-07 8.24E-08 9.77E-06College attainment Conserved_LindbladToh 16.32 3.31 1.61E-07 3.92E-08 1.98E-05